Searching for Best Direction in Healthcare: Distilling Opportunities, Priorities and Responsibilities
Bibliographic record
Abstract
Canada's health and its care are evolving. Evidence from serial Health Care in Canada surveys of the public and health professionals over the last two decades reveal a persistent sense of care quality, despite an aging population, decreasing levels of good and excellent health, increasing prevalence of chronic illnesses; and sub-optimal access to timely and patient-centred care. Stakeholders are, however, somewhat pessimistic and many sense complete rebuilding, or major changes, may be necessary. To improve access, the primary health concern of all Canadians - increasing medical and nursing school enrolment, and requiring professionals to work in teams - have attracted increasingly high support from both the public and professionals. However, physicians' support lags behind that of nursing, pharmacy and administrative colleagues; and, currently, only a minority of patients and professionals are actively involved in team care programs. Another example in which high levels of support may not necessarily translate into priority implementation of promising interventions is the realm of patient-centred care. The public and all professionals report a very high level of general support for care provided in a caring and respectful manner. However, while the public rank it second in implementation priority, following timely access, the majority of professionals rank it only fourth. By contrast, there is remarkable pan-stakeholder concordance around interventions to improve the overall health system, with the majority of public and professional stakeholders rating the creation of national supply systems as their top priority to expedite the clinical and cost efficiency of new treatments. There is a similar pan-stakeholder concordance around priority of responsibility to drive innovations, the top three being: federal/provincial governments; research hospitals/regional health authorities; and the pharmaceutical industry. In summary, Canadians are at a healthcare crossroads. Population health is decreasing, chronic diseases are increasing and desire for timely access to patient-centred, team-delivered and technology-supported care remain top concerns. Despite some disconnects between theoretical support for, and priority to implement, promising innovations, there is universal support to optimize resources to make things better. And there is concordance around the leadership best suited to lead innovation. Things can be better.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.049 | 0.056 |
| Scholarly communication | 0.046 | 0.021 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.007 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".